Experimental study of intelligent controllers under uncertainty using type-1 and type-2 fuzzy logic

  • Authors:

  • Roberto Sepúlveda;Oscar Castillo;Patricia Melin;Antonio Rodríguez-Díaz;Oscar Montiel

  • Affiliations:

  • CITEDI-IPN, Av. del Parque #1310, Mesa de Otay Tijuana, B.C., Mexico;Department of Computer Science, Tijuana Institute of Technology, Tijuana, B.C., Mexico;Department of Computer Science, Tijuana Institute of Technology, Tijuana, B.C., Mexico;FCQI-UABC, Tijuana, B.C., Mexico;CITEDI-IPN, Av. del Parque #1310, Mesa de Otay Tijuana, B.C., Mexico

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2007

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Abstract

Uncertainty is an inherent part in control systems used in real world applications. The use of new methods for handling incomplete information is of fundamental importance. Type-1 fuzzy sets used in conventional fuzzy systems cannot fully handle the uncertainties present in control systems. Type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such uncertainties in a better way because they provide us with more parameters and more design degrees of freedom. This paper deals with the design of control systems using type-2 fuzzy logic for minimizing the effects of uncertainty produced by the instrumentation elements, environmental noise, etc. The experimental results are divided in two classes, in the first class, simulations of a feedback control system for a non-linear plant using type-1 and type-2 fuzzy logic controllers are presented; a comparative analysis of the systems' response in both cases was performed, with and without the presence of uncertainty. For the second class, a non-linear identification problem for time-series prediction is presented. Based on the experimental results the conclusion is that the best results are obtained using type-2 fuzzy systems.